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xlm-roberta-base-esp-finetuned
Kuongan/xlm-roberta-base-esp-finetuned
xlm-roberta-base-esp-finetuned is a text classification model from Kuongan. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Jan 29, 2025
- Updated
- Jan 29, 2025
- SHA
- 80b5a967
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
xlm-roberta-base-esp-finetuned
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3345
- F1: 0.7715
- Roc Auc: 0.8559
- Accuracy: 0.6033
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.556 | 1.0 | 98 | 0.4924 | 0.1173 | 0.5484 | 0.125 |
| 0.3942 | 2.0 | 196 | 0.3753 | 0.6280 | 0.7772 | 0.4293 |
| 0.3051 | 3.0 | 294 | 0.3283 | 0.7282 | 0.8250 | 0.5380 |
| 0.2566 | 4.0 | 392 | 0.3234 | 0.7277 | 0.8307 | 0.5380 |
| 0.2077 | 5.0 | 490 | 0.3109 | 0.7502 | 0.8392 | 0.5652 |
| 0.1646 | 6.0 | 588 | 0.3135 | 0.7383 | 0.8336 | 0.5435 |
| 0.1524 | 7.0 | 686 | 0.3132 | 0.7456 | 0.8359 | 0.5707 |
| 0.1346 | 8.0 | 784 | 0.3253 | 0.7427 | 0.8341 | 0.5380 |
| 0.1076 | 9.0 | 882 | 0.3272 | 0.7549 | 0.8457 | 0.5924 |
| 0.0963 | 10.0 | 980 | 0.3384 | 0.7671 | 0.8528 | 0.5978 |
| 0.0888 | 11.0 | 1078 | 0.3381 | 0.7620 | 0.8485 | 0.5870 |
| 0.0762 | 12.0 | 1176 | 0.3345 | 0.7715 | 0.8559 | 0.6033 |
| 0.0528 | 13.0 | 1274 | 0.3566 | 0.7683 | 0.8577 | 0.5924 |
| 0.0512 | 14.0 | 1372 | 0.3522 | 0.7643 | 0.8534 | 0.5924 |
| 0.0435 | 15.0 | 1470 | 0.3595 | 0.7635 | 0.8517 | 0.5978 |
| 0.0415 | 16.0 | 1568 | 0.3651 | 0.7646 | 0.8550 | 0.5870 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jan 29, 2025
- Updated
- Jan 29, 2025